When to use (or not) AI

Guidance from Google's People + AI Guidebook about situations where an AI approach is probably better than a rule-based approach, and some in which it is not.

When AI is probably better:

  1. Recommending different content to different users. Such as providing personalized suggestions for movies to watch.
  2. Prediction of future events. For example, showing flight prices for a trip to Denver in late November.
  3. Personalization improves the user experience. Personalizing automated home thermostats makes homes more comfortable and the thermostats more efficient over time.
  4. Natural language understanding. Dictation software requires AI to function well for different languages and speech styles.
  5. Recognition of an entire class of entities. It’s not possible to program every single face into a photo tagging app — it uses AI to recognize two photos as the same person.
  6. Detection of low occurrence events that change over time. Credit card fraud is constantly evolving and happens infrequently to individuals, but frequently across a large group. AI can learn these evolving patterns and detect new kinds of fraud as they emerge.
  7. An agent or bot experience for a particular domain. Booking a hotel follows a similar pattern for a large number of users and can be automated to expedite the process.
  8. Showing dynamic content is more efficient than a predictable interface. AI-generated suggestions from a streaming service surface new content that would be nearly impossible for a user to find otherwise.

When AI is probably not better:

  1. Maintaining predictability. Sometimes the most valuable part of the core experience is its predictability, regardless of context or additional user input. For example, a “Home” or “Cancel” button is easier to use as an escape hatch when it stays in the same place.
  2. Providing static or limited information. For example, a credit card entry form is simple, standard, and doesn’t have highly varied information requirements for different users.
  3. Minimizing costly errors. If the cost of errors is very high and outweighs the benefits of a small increase in success rate, such as a navigation guide that suggests an off-road route to save a few seconds of travel time.
  4. Complete transparency. If users, customers, or developers need to understand precisely everything that happens in the code, like with Open Source Software. AI can’t always deliver that level of explainability.
  5. Optimizing for high speed and low cost. If speed of development and getting to market first is more important than anything else to the business, including the value that adding AI would provide.
  6. Automating high-value tasks. If people explicitly tell you they don’t want a task automated or augmented with AI, that’s a good task not to try to disrupt. 
AI is well-suited for applications like:

  • Recommending different content to different users, such as movie suggestions
  • Predicting future events, such as weather events or flight price changes
  • Natural language understanding
  • Image recognition

A rule or heuristic-based solution involving manual control may be better when:

  • Maintaining predictability is important
  • Users, customers or developers need complete transparency
  • People don’t want a task automated

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